Chen Jindong, Du Yuxuan, Liu Linlin, Zhang Pinyi, Zhang Wen
School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China.
Beijing Key Lab of Green Development Decision Based on Big Data, Beijing 100192, China.
Entropy (Basel). 2019 Jan 12;21(1):57. doi: 10.3390/e21010057.
The modeling and forecasting of BBS (Bulletin Board System) posts time series is crucial for government agencies, corporations and website operators to monitor public opinion. Accurate prediction of the number of BBS posts will assist government agencies or corporations in making timely decisions and estimating the future number of BBS posts will help website operators to allocate resources to deal with the possible hot events pressure. By combining sample entropy (SampEn) and deep neural networks (DNN), an approach (SampEn-DNN) is proposed for BBS posts time series modeling and forecasting. The main idea of SampEn-DNN is to utilize SampEn to decide the input vectors of DNN with smallest complexity, and DNN to enhance the prediction performance of time series. Selecting Tianya Zatan new posts as the data source, the performances of SampEn-DNN were compared with auto-regressive integrated moving average (ARIMA), seasonal ARIMA, polynomial regression, neural networks, etc. approaches for prediction of the daily number of new posts. From the experimental results, it can be found that the proposed approach SampEn-DNN outperforms the state-of-the-art approaches for BBS posts time series modeling and forecasting.
对政府机构、企业和网站运营商来说，论坛（电子公告板系统）帖子时间序列的建模与预测对于监测舆情至关重要。准确预测论坛帖子数量将有助于政府机构或企业及时做出决策，而预估论坛帖子的未来数量则能帮助网站运营商分配资源以应对可能出现的热点事件压力。通过结合样本熵（SampEn）和深度神经网络（DNN），提出了一种用于论坛帖子时间序列建模与预测的方法（SampEn-DNN）。SampEn-DNN的主要思想是利用样本熵来确定复杂度最小的深度神经网络输入向量，并利用深度神经网络提高时间序列的预测性能。以天涯杂谈新帖作为数据源，将SampEn-DNN的性能与自回归积分滑动平均模型（ARIMA）、季节性自回归积分滑动平均模型、多项式回归、神经网络等方法进行比较，以预测每日新帖数量。从实验结果可以发现，所提出的SampEn-DNN方法在论坛帖子时间序列建模与预测方面优于现有方法。
